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Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering

Overview of attention for article published in Computational Statistics, August 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#20 of 182)
  • High Attention Score compared to outputs of the same age (82nd percentile)

Mentioned by

twitter
1 X user
patent
2 patents
googleplus
1 Google+ user

Citations

dimensions_citation
44 Dimensions

Readers on

mendeley
61 Mendeley
citeulike
1 CiteULike
Title
Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering
Published in
Computational Statistics, August 2012
DOI 10.1007/s00180-012-0352-y
Authors

Isambi S. Mbalawata, Simo Särkkä, Heikki Haario

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 2 3%
Australia 1 2%
Germany 1 2%
Denmark 1 2%
Japan 1 2%
Unknown 55 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 38%
Researcher 8 13%
Professor 5 8%
Student > Doctoral Student 4 7%
Student > Master 4 7%
Other 7 11%
Unknown 10 16%
Readers by discipline Count As %
Engineering 17 28%
Mathematics 9 15%
Computer Science 5 8%
Agricultural and Biological Sciences 2 3%
Business, Management and Accounting 2 3%
Other 9 15%
Unknown 17 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 November 2022.
All research outputs
#4,366,290
of 24,195,945 outputs
Outputs from Computational Statistics
#20
of 182 outputs
Outputs of similar age
#29,047
of 167,277 outputs
Outputs of similar age from Computational Statistics
#2
of 4 outputs
Altmetric has tracked 24,195,945 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 182 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 167,277 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.